Determination of boron with molecular emission using laser-induced breakdown spectroscopy combined with laser-induced radical fluorescence

Fresh Meat Classification Using Laser-Induced Breakdown Spectroscopy Assisted by LightGBM and Optuna

To enhance the accuracy of identifying fresh meat varieties using laser-induced breakdown spectroscopy (LIBS), we utilized the LightGBM model in combination with the Optuna algorithm. The procedure involved flattening fresh meat slices with glass slides and collecting spectral data of the plasma from the surfaces of the fresh meat tissues (pork, beef, and chicken) using LIBS technology. A total of 900 spectra were collected. Initially, we established LightGBM and SVM (support vector machine) models for the collected spectra. Subsequently, we applied information gain and peak extraction algorithms to select the features for each model. We then employed Optuna to optimize the hyperparameters of the LightGBM model, while a 10-fold cross-validation was conducted to determine the optimal parameters for SVM. Ultimately, the LightGBM model achieved higher accuracy, macro-F1, and Cohen's kappa coefficient (kappa coefficient) values of 0.9370, 0.9364, and 0.9244, respectively, compared to the SVM model's values of 0.8888, 0.8881, and 0.8666. This study provides a novel method for the rapid classification of fresh meat varieties using LIBS.

Laser Ablation Saturated Absorption Spectroscopy (LA-SAS) for In Situ Detection of Neodymium Isotopes

This study introduces a novel optical system integrating laser ablation with saturated absorption spectroscopy (LA-SAS) for the detection of neodymium isotopes, crucial for the characterization process in nuclear forensics. Conventional methods for isotope analysis have encountered challenges, such as the inability to perform on-site detection or difficulty in distinguishing minor isotope differences. The LA-SAS system overcomes these limitations by combining pulsed laser for ablation and counter-propagated diode laser for saturated absorption, enabling preparation-free detection with enhanced spectral resolution. The analytical capability was demonstrated through the successful detection of seven neodymium isotopes (142Nd, 143Nd, 144Nd, 145Nd, 146Nd, 148Nd, and 150Nd) with a line width narrowed to 0.1 pm, significantly improving upon the resolution limit of conventional LA-based methods. In addition, quantitative analysis of isotope abundance was facilitated by evaluating the signals from saturated absorption spectra. Special attention was given to the hyperfine structure of odd isotopes, which was resolved by multiple fitting in spectra, thereby refining the accuracy of isotope quantification up to an average bias of 0.45%. The established LA-SAS system offers on-site detection capability based on LA, and also the high resolution from SAS, making it a promising method for in situ nuclear forensics. Consequently, the study enhances the academic understanding of neodymium isotopes and underscores the potential of LA-SAS in fields requiring detailed isotopic information.

Identification of Graves' ophthalmology by laser-induced breakdown spectroscopy combined with machine learning method

Diagnosis of the Graves' ophthalmology remains a significant challenge. We identified between Graves' ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy (LIBS) combined with machine learning method. In this work, the paraffin-embedded samples of the Graves' ophthalmology were prepared for LIBS spectra acquisition. The metallic elements (Na, K, Al, Ca), non-metallic element (O) and molecular bands ((C-N), (C-O)) were selected for diagnosing Graves' ophthalmology. The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (kNN), and generalized regression neural network (GRNN), respectively. The results showed that the predicted accuracy rates of LDA, SVM, kNN, GRNN were 76.33%, 96.28%, 96.56%, and 96.33%, respectively. The sensitivity of four models were 75.89%, 93.78%, 96.78%, and 96.67%, respectively. The specificity of four models were 76.78%, 98.78%, 96.33%, and 96.00%, respectively. This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves' ophthalmopathy with a higher rate of accuracy. The kNN had the best performance by comparing the three nonlinear models. Therefore, LIBS combined with machine learning method can be an effective way to discriminate Graves' ophthalmology.

Quantitative measurement of hydrogen isotopes in titanium using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) has been considered a promising technology for nuclear safeguard inspection, especially for isotope content ratio determination, since it can be easily designed for portable, fast, and in situ measurement. However, it was a challenge to determine hydrogen isotopes in metal samples due to the unfavorable spectral interference, the poor calibration of the hydrogen content, and the small difference between the atomic emission intensity of hydrogen isotopes at around 656.28 nm. This paper presents the determination of hydrogen isotope contents ratio using LIBS under partially baseline-resolved conditions. The results show that by introducing a proper buffer atmosphere for the LIBS measurement, the resolution of the hydrogen and deuterium emissions could be improved, but still not enabled, by a baseline resolution with a moderate resolution spectrometer. However, with the method of integral intensity correction, the accurate quantitative measurement of hydrogen and deuterium contents in a metal matrix could be achieved. This work provided the possibilities for the further development of LIBS in hydrogen isotopes in in situ measurement for nuclear safeguards.

Accurate quantification of alkalinity of sintered ore by random forest model based on PCA and variable importance (PCA-VI-RF)

The alkalinity of sintered ore has an important impact on the quality, output, and energy consumption of blast furnace smelting, and there is an urgent need for a method for accurate quantifying of the alkalinity of sintered ore. The present work explores the combination of the laser-induced breakdown spectroscopy (LIBS) technique and random forest (RF) based on principal component analysis (PCA) and variable importance for the quantitative analysis of the alkalinity of sintered ore. Sixteen sintered ore samples were used in this study, and the characteristic lines of LIBS spectra for sintered ore samples can be identified based on the National Institute of Standards and Technology (NIST) database. At first, abnormal spectra are identified and rejected by PCA coupled with Mahalanobis distance (MD). Then, the input variable for the RF calibration model is optimized according to the variable importance threshold obtained by the RF model, and two RF model parameters of ${{n}_{\rm tree}}$n_tree and ${{m}_{\rm try}}$m_try are determined by out-of-bag estimate. Finally, the PCA-VI-RF model is built under the optimal model parameters. In order to verify the predictive ability of the quantitative model, the PCA-VI-RF model prediction results were compared with the RF model, partial least-squares model, and least-squares support vector machine model. The result demonstrated that PCA-VI-RF shows better analytical performance than other methods. Compared with the RF model with the original spectrum as input, the averaged relative errors of test results decreased from 5.82% to 3.94%, coefficients of determination (${R^2}$R²) of the test set increased from 0.8957 to 0.9814, and the root mean square error decreased from 0.1502% to 0.0860%. The speed of modeling and prediction has also been greatly improved, and the modeling time was reduced from 4675.56 to 16.86 s. The stability of the PCA-VI-RF model was verified by the relative standard deviation (RSD) of the test data prediction results, and the RSD reached below 4.74%. This study shows LIBS combining PCA-VI-RF is an effective method for accurate quantification of the alkalinity of sintered ore. It has great significance for the potential application of real-time online analysis of the alkalinity of sintered ore.

Improving the spectral qualities of major elements in soil by controlling the ambient pressure in time-resolved laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) is a powerful tool in the soil monitoring field, but the poor spectral quality limits its application. To improve the spectral quality of major elements in soil samples, a method based on controlling the ambient pressure and time sequence was introduced. Spectral qualities that include signal-to-background ratio (SBR), spectral stability, and spectral profile were all studied in different ambient pressures and delay times. The results show that the SBRs of Na and K elements increased from 20 to about 300, when the air pressure and delay time were controlled. Meanwhile, the relative standard deviations were improved from more than 30% to less than 5% due to the release of the self-absorption effect. This work proved that the spectral qualities of LIBS can be improved a lot by controlling the ambient pressure in the field of detecting major elements in soil.

Experimental investigation of laser-induced breakdown spectroscopy assisted with laser-induced fluorescence for trace aluminum detection in steatite ceramics

Laser-induced breakdown spectroscopy (LIBS) assisted with laser-induced fluorescence (LIF) was introduced to detect trace aluminum in steatite ceramics in this work. The mechanism and transition process of laser-induced aluminum atomic fluorescence in laser-induced plasma was described and discussed. Selective enhancement of LIF and temporal synchronicity between radiation laser and fluorescence were studied. The influences of ablation laser energy, power density of the radiation laser, and interpulse delay were experimentally investigated. The results showed that 60 mJ in ablation laser energy and 4 μs in interpulse delay were the optimal choice for fluorescent intensity. The fluorescence was increased to the saturation level over 4  MW/cm². Spectral stability improvement of LIBS-LIF was also discovered in this work. The results proved that LIBS-LIF is a feasible and effective modification of LIBS for ceramics analysis.

Isotopic determination with molecular emission using laser-induced breakdown spectroscopy and laser-induced radical fluorescence

Molecular emission can be used for isotopic analysis in laser-induced breakdown spectroscopy (LIBS) due to its large isotopic shift. However, spectral weakness and interference have become the main flaws in molecular isotopic analysis, causing deterioration of quantitative accuracy and sensitivity. Here, to overcome these problems, laser-induced radical fluorescence (LIRF) was applied to enhance the molecular spectra and eliminate the spectral interference. The root mean square errors of cross validation (RMSECVs) of boron and carbon isotopes (¹¹BO, ¹⁰BO, ¹²CN, and ¹³CN) improved to 2.632, 5.721, 5.990, and 1.543 at.%, as compared with 16.96, 35.79, 57.10, and 13.89 at.%, respectively, obtained in the case without LIRF. The limits of detection (LoDs) of ¹¹BO, ¹⁰BO, ¹²CN, and ¹³CN were 0.9858, 0.8470, 1.606, and 1.193 at.%, respectively. This work demonstrates the feasibility of LIBS-LIRF to achieve isotopic determination with high accuracy and sensitivity.

Determination of potassium in ceramic raw materials using laser-induced breakdown spectroscopy combined with profile fitting

The determination of potassium (K) content in ceramic raw materials provides important references for ceramic sintering. To realize rapid, in situ, and real-time analysis detection, a laser-induced breakdown spectroscopy (LIBS) system was set up to analyze K content in ceramic raw materials. However, the self-absorption was the serious influence on the accuracy of K element analysis. In this work, a method of profile fitting with Lorentz function was proposed to reduce the self-absorption effect in LIBS. After Lorentz fitting, the determination coefficient (R² factor) for K element improved from 0.993 to 0.998, the root mean square error of cross-validation reduced from 0.458 wt. % to 0.145 wt. %, and the average relative error reduced from 13.769% to 5.121%. The results indicate that the Lorentz fitting can effectively reduce the self-absorption effect, and improve the accuracy of quantitative analysis for K element. According to the results, the proposed approach can be a promising method for determination of elements that suffer from self-absorption in LIBS.